Testing discharge assimilation strategies to enhance short-range AI-based operational rainfall–runoff forecasts

Saint-Fleur, Bob E.; Gaume, Eric; Surmont, Florian; Akil, Nicolas; Theriez, Dominique

doi:10.5194/hess-30-3497-2026

Articles | Volume 30, issue 11

https://doi.org/10.5194/hess-30-3497-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-30-3497-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 30, issue 11

Research article

|

11 Jun 2026

Research article |

| 11 Jun 2026

Testing discharge assimilation strategies to enhance short-range AI-based operational rainfall–runoff forecasts

Bob E. Saint-Fleur, Eric Gaume, Florian Surmont, Nicolas Akil, and Dominique Theriez

Data sets

Data (raw and processed) to "Testing data assimilation strategies to enhance short-range AI-based discharge forecasts Bob E. Saint-Fleur and Eric Gaume https://doi.org/10.5281/zenodo.16944643

Model code and software

AI_Operational_HydroForecast Bob E. Saint-Fleur https://doi.org/10.5281/zenodo.20415493

Short summary

This paper highlights the importance of discharge assimilation (DA) for artificial intelligence (AI)-based operational discharge forecasting. Using two public datasets from France and the USA, simulated discharge from two rainfall-runoff models, and a multilayer perceptron for implementation, we evaluate three DA strategies under both deterministic and probabilistic forecasting approaches. Results show that DA is crucial and that model performance may decrease between the two forecasting cases.